Paul C. Lauterbur Research Center for Biomedical Imaging, Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences, SZ University Town, Shenzhen, China; Shenzhen College of Advanced Technology, University of Chinese Academy of Sciences, Beijing, China.
Paul C. Lauterbur Research Center for Biomedical Imaging, Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences, SZ University Town, Shenzhen, China.
JACC Cardiovasc Imaging. 2024 Aug;17(8):880-893. doi: 10.1016/j.jcmg.2024.04.013. Epub 2024 Jul 10.
The cumulative burden of hypertrophic cardiomyopathy (HCM) is significant, with a noteworthy percentage (10%-15%) of patients with HCM per year experiencing major adverse cardiovascular events (MACEs). A current risk stratification scheme for HCM had only limited accuracy in predicting sudden cardiac death (SCD) and failed to account for a broader spectrum of adverse cardiovascular events and cardiac magnetic resonance (CMR) parameters.
This study sought to develop and evaluate a machine learning (ML) framework that integrates CMR imaging and clinical characteristics to predict MACEs in patients with HCM.
A total of 758 patients with HCM (67% male; age 49 ± 14 years) who were admitted between 2010 and 2017 from 4 medical centers were included. The ML model was built on the internal discovery cohort (533 patients with HCM, admitted to Fuwai Hospital, Beijing, China) by using the light gradient-boosting machine and internally evaluated using cross-validation. The external test cohort consisted of 225 patients with HCM from 3 medical centers. A total of 14 CMR imaging features (strain and late gadolinium enhancement [LGE]) and 23 clinical variables were evaluated and used to inform the ML model. MACEs included a composite of arrhythmic events, SCD, heart failure, and atrial fibrillation-related stroke.
MACEs occurred in 191 (25%) patients over a median follow-up period of 109.0 months (Q1-Q3: 73.0-118.8 months). Our ML model achieved areas under the curve (AUCs) of 0.830 and 0.812 (internally and externally, respectively). The model outperformed the classic HCM Risk-SCD model, with significant improvement (P < 0.001) of 22.7% in the AUC. Using the cubic spline analysis, the study showed that the extent of LGE and the impairment of global radial strain (GRS) and global circumferential strain (GCS) were nonlinearly correlated with MACEs: an elevated risk of adverse cardiovascular events was observed when these parameters reached the high enough second tertiles (11.6% for LGE, 25.8% for GRS, -17.3% for GCS).
ML-empowered risk stratification using CMR and clinical features enabled accurate MACE prediction beyond the classic HCM Risk-SCD model. In addition, the nonlinear correlation between CMR features (LGE and left ventricular pressure gradient) and MACEs uncovered in this study provides valuable insights for the clinical assessment and management of HCM.
肥厚型心肌病（HCM）的累积负担很大，每年有相当比例（10%-15%）的 HCM 患者发生主要不良心血管事件（MACEs）。目前的 HCM 风险分层方案在预测心源性猝死（SCD）方面仅有有限的准确性，并且未能考虑到更广泛的不良心血管事件和心脏磁共振（CMR）参数。
本研究旨在开发和评估一种机器学习（ML）框架，该框架将 CMR 成像和临床特征相结合，以预测 HCM 患者的 MACEs。
共纳入 758 名 HCM 患者（67%为男性；年龄 49±14 岁），他们于 2010 年至 2017 年期间在 4 家医疗中心入院。ML 模型在内部发现队列（533 名 HCM 患者，来自中国北京阜外医院）中通过使用轻梯度提升机进行构建，并通过交叉验证进行内部评估。外部测试队列由来自 3 家医疗中心的 225 名 HCM 患者组成。评估并使用了总共 14 种 CMR 成像特征（应变和晚期钆增强[LGE]）和 23 种临床变量来构建 ML 模型。MACEs包括心律失常事件、SCD、心力衰竭和与心房颤动相关的中风的复合事件。
中位随访 109.0 个月（Q1-Q3：73.0-118.8 个月）期间，191 名（25%）患者发生了 MACEs。我们的 ML 模型在内部和外部的曲线下面积（AUCs）分别为 0.830 和 0.812。该模型优于经典的 HCM Risk-SCD 模型，AUC 显著提高（P<0.001）了 22.7%。使用三次样条分析，研究表明 LGE 的程度以及整体径向应变（GRS）和整体周向应变（GCS）的损害与 MACEs 呈非线性相关：当这些参数达到足够高的第二三分位数时，不良心血管事件的风险会增加（LGE 为 11.6%，GRS 为 25.8%，GCS 为-17.3%）。
基于 CMR 和临床特征的 ML 赋能风险分层能够在经典的 HCM Risk-SCD 模型之外准确预测 MACEs。此外，本研究中发现的 CMR 特征（LGE 和左心室压力梯度）与 MACEs 之间的非线性相关性为 HCM 的临床评估和管理提供了有价值的见解。
